milvus/internal/util/function/openai_text_embedding_provi...

207 lines
6.0 KiB
Go

/*
* # Licensed to the LF AI & Data foundation under one
* # or more contributor license agreements. See the NOTICE file
* # distributed with this work for additional information
* # regarding copyright ownership. The ASF licenses this file
* # to you under the Apache License, Version 2.0 (the
* # "License"); you may not use this file except in compliance
* # with the License. You may obtain a copy of the License at
* #
* # http://www.apache.org/licenses/LICENSE-2.0
* #
* # Unless required by applicable law or agreed to in writing, software
* # distributed under the License is distributed on an "AS IS" BASIS,
* # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* # See the License for the specific language governing permissions and
* # limitations under the License.
*/
package function
import (
"encoding/json"
"fmt"
"net/http"
"net/http/httptest"
"os"
"testing"
"github.com/stretchr/testify/suite"
"github.com/milvus-io/milvus-proto/go-api/v2/commonpb"
"github.com/milvus-io/milvus-proto/go-api/v2/schemapb"
"github.com/milvus-io/milvus/internal/util/function/models/openai"
)
func TestOpenAITextEmbeddingProvider(t *testing.T) {
suite.Run(t, new(OpenAITextEmbeddingProviderSuite))
}
type OpenAITextEmbeddingProviderSuite struct {
suite.Suite
schema *schemapb.CollectionSchema
providers []string
}
func (s *OpenAITextEmbeddingProviderSuite) SetupTest() {
s.schema = &schemapb.CollectionSchema{
Name: "test",
Fields: []*schemapb.FieldSchema{
{FieldID: 100, Name: "int64", DataType: schemapb.DataType_Int64},
{FieldID: 101, Name: "text", DataType: schemapb.DataType_VarChar},
{
FieldID: 102, Name: "vector", DataType: schemapb.DataType_FloatVector,
TypeParams: []*commonpb.KeyValuePair{
{Key: "dim", Value: "4"},
},
},
},
}
s.providers = []string{openAIProvider, azureOpenAIProvider}
}
func createOpenAIProvider(url string, schema *schemapb.FieldSchema, providerName string) (textEmbeddingProvider, error) {
functionSchema := &schemapb.FunctionSchema{
Name: "test",
Type: schemapb.FunctionType_Unknown,
InputFieldNames: []string{"text"},
OutputFieldNames: []string{"vector"},
InputFieldIds: []int64{101},
OutputFieldIds: []int64{102},
Params: []*commonpb.KeyValuePair{
{Key: modelNameParamKey, Value: "text-embedding-ada-002"},
{Key: apiKeyParamKey, Value: "mock"},
{Key: dimParamKey, Value: "4"},
{Key: embeddingURLParamKey, Value: url},
},
}
switch providerName {
case openAIProvider:
return NewOpenAIEmbeddingProvider(schema, functionSchema)
case azureOpenAIProvider:
return NewAzureOpenAIEmbeddingProvider(schema, functionSchema)
default:
return nil, fmt.Errorf("Unknow provider")
}
}
func (s *OpenAITextEmbeddingProviderSuite) TestEmbedding() {
ts := CreateOpenAIEmbeddingServer()
defer ts.Close()
for _, provderName := range s.providers {
provder, err := createOpenAIProvider(ts.URL, s.schema.Fields[2], provderName)
s.NoError(err)
{
data := []string{"sentence"}
ret, err2 := provder.CallEmbedding(data, InsertMode)
s.NoError(err2)
s.Equal(1, len(ret))
s.Equal(4, len(ret[0]))
s.Equal([]float32{0.0, 0.1, 0.2, 0.3}, ret[0])
}
{
data := []string{"sentence 1", "sentence 2", "sentence 3"}
ret, _ := provder.CallEmbedding(data, SearchMode)
s.Equal([][]float32{{0.0, 0.1, 0.2, 0.3}, {1.0, 1.1, 1.2, 1.3}, {2.0, 2.1, 2.2, 2.3}}, ret)
}
}
}
func (s *OpenAITextEmbeddingProviderSuite) TestEmbeddingDimNotMatch() {
ts := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
var res openai.EmbeddingResponse
res.Object = "list"
res.Model = "text-embedding-3-small"
res.Data = append(res.Data, openai.EmbeddingData{
Object: "embedding",
Embedding: []float32{1.0, 1.0, 1.0, 1.0},
Index: 0,
})
res.Data = append(res.Data, openai.EmbeddingData{
Object: "embedding",
Embedding: []float32{1.0, 1.0},
Index: 1,
})
res.Usage = openai.Usage{
PromptTokens: 1,
TotalTokens: 100,
}
w.WriteHeader(http.StatusOK)
data, _ := json.Marshal(res)
w.Write(data)
}))
defer ts.Close()
for _, provderName := range s.providers {
provder, err := createOpenAIProvider(ts.URL, s.schema.Fields[2], provderName)
s.NoError(err)
// embedding dim not match
data := []string{"sentence", "sentence"}
_, err2 := provder.CallEmbedding(data, InsertMode)
s.Error(err2)
}
}
func (s *OpenAITextEmbeddingProviderSuite) TestEmbeddingNubmerNotMatch() {
ts := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
var res openai.EmbeddingResponse
res.Object = "list"
res.Model = "text-embedding-3-small"
res.Data = append(res.Data, openai.EmbeddingData{
Object: "embedding",
Embedding: []float32{1.0, 1.0, 1.0, 1.0},
Index: 0,
})
res.Usage = openai.Usage{
PromptTokens: 1,
TotalTokens: 100,
}
w.WriteHeader(http.StatusOK)
data, _ := json.Marshal(res)
w.Write(data)
}))
defer ts.Close()
for _, provderName := range s.providers {
provder, err := createOpenAIProvider(ts.URL, s.schema.Fields[2], provderName)
s.NoError(err)
// embedding dim not match
data := []string{"sentence", "sentence2"}
_, err2 := provder.CallEmbedding(data, InsertMode)
s.Error(err2)
}
}
func (s *OpenAITextEmbeddingProviderSuite) TestCreateOpenAIEmbeddingClient() {
_, err := createOpenAIEmbeddingClient("", "")
s.Error(err)
os.Setenv(openaiAKEnvStr, "mockKey")
defer os.Unsetenv(openaiAKEnvStr)
_, err = createOpenAIEmbeddingClient("", "")
s.NoError(err)
}
func (s *OpenAITextEmbeddingProviderSuite) TestCreateAzureOpenAIEmbeddingClient() {
_, err := createAzureOpenAIEmbeddingClient("", "")
s.Error(err)
os.Setenv(azureOpenaiAKEnvStr, "mockKey")
defer os.Unsetenv(azureOpenaiAKEnvStr)
_, err = createAzureOpenAIEmbeddingClient("", "")
s.Error(err)
os.Setenv(azureOpenaiResourceName, "mockResource")
defer os.Unsetenv(azureOpenaiResourceName)
_, err = createAzureOpenAIEmbeddingClient("", "")
s.NoError(err)
}